Hierarchical multi-agent planning for flexible assembly of large-scale lunar facilities. (January 2023) - Xu, Rui; Zhao, Yuting; Li, Zhaoyu; Zhu, Shengying; Liang, Zixuan; Gao, Yue
Highlights: An autonomous multi-robot planning method is proposed for the flexible assembly of large-scale lunar facilities. A distributed multi-agent planning frame with multi-replica public agents is introduced. The hierarchical task graph (HTG) is proposed to help with suspending the mission at a relatively safe state when the environment is not suitable to work. The Hierarchical Multi-Agent Planning method based on Two-stage Two-side Matching (HMAP-TTM) is proposed to optimize the time of the flexible assembly mission. Compared to the baseline methods, HMAP-TTM can generate plans with shorter mission time and require smaller communication costs. Abstract: Several national space agencies and commercial aerospace companies plan to set up lunar bases with large-scale facilities that rely on multiple lunar robots' assembly. Mission planning is necessary to achieve efficient multi-robot cooperation. This paper aims at autonomous multi-robot planning for the flexible assembly of the large-scale lunar facility, considering the harsh lunar environment, mission time optimization, and joint actions. The lunar robots and modules are scattered around the mission area without fixed assembly lines. Thus, the traditional assembly planning methods ignoring the optimal selection of modules are unable to handle this problem. We propose a hierarchical multi-agent planning method based on two-stage two-sided matching (HMAP-TTM) to solve this critical problem. First, the distributed planning framework with multi-replica public agents is introduced, ensuring robot plan knowledge consistency through public agents' communication. Second, the hierarchical task graph (HTG) divides the mission into task layers based on task dependency knowledge. Third, we develop a novel two-stage two-sided matching algorithm. Time-optimal plans emerge from the matching games among public and private agents in each layer of HTG. Agents make decisions in the game based on action knowledge updated during planning. Finally, an assembly mission is presented to prove the method's effectiveness. The simulation results show that the HMAP-TTM can generate plans with shorter mission time and require smaller communication costs than the baseline methods.